ANCOVA
in Reading Irregular Correct Words (Reading Irregular Correct
Words)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Reading Irregular Correct Words (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Reading Irregular Correct Words (measured using pre- and
post-tests).
Setting Initial Variables
dv = "score.CI"
dv.pos = "score.CI.pos"
dv.pre = "score.CI.pre"
fatores2 <- c("genero","zona.participante","zona.escola","score.CI.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["genero"]] = c("#FF007F","#4D4DFF")
color[["zona.escola"]] = c("#AA00FF","#00CCCC")
color[["zona.participante"]] = c("#AA00FF","#00CCCC")
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["genero"]] = c("F","M")
level[["zona.escola"]] = c("Rural","Urbana")
level[["zona.participante"]] = c("Rural","Urbana")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:genero"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:zona.escola"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:zona.participante"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
for (coln in c(
"palavras.lidas","score.compreensao","tri.compreensao",
"score.vocab","tri.vocab",
"score.vocab.ensinado","tri.vocab.ensinado","score.vocab.nao.ensinado","tri.vocab.nao.ensinado",
"score.CLPP","tri.CLPP","score.CR","tri.CR",
"score.CI","tri.CI","score.TV","tri.TV","score.TF","tri.TF","score.TO","tri.TO")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "leitura.st")
dat <- gdat
dat$grupo <- factor(dat[["grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.CI.pre |
50 |
9.300 |
10.0 |
0 |
14 |
3.512 |
0.497 |
0.998 |
4.00 |
NO |
-0.962 |
0.427 |
| Experimental |
|
|
|
|
score.CI.pre |
42 |
8.667 |
9.0 |
0 |
14 |
3.490 |
0.538 |
1.088 |
4.00 |
NO |
-0.574 |
0.210 |
|
|
|
|
|
score.CI.pre |
92 |
9.011 |
10.0 |
0 |
14 |
3.497 |
0.365 |
0.724 |
4.00 |
NO |
-0.788 |
0.323 |
| Controle |
|
|
|
|
score.CI.pos |
50 |
8.180 |
8.0 |
0 |
17 |
4.498 |
0.636 |
1.278 |
6.00 |
YES |
-0.192 |
-0.585 |
| Experimental |
|
|
|
|
score.CI.pos |
42 |
9.643 |
10.0 |
0 |
17 |
4.282 |
0.661 |
1.334 |
5.75 |
NO |
-0.768 |
-0.122 |
|
|
|
|
|
score.CI.pos |
92 |
8.848 |
9.0 |
0 |
17 |
4.437 |
0.463 |
0.919 |
6.00 |
YES |
-0.447 |
-0.481 |
| Controle |
F |
|
|
|
score.CI.pre |
25 |
9.240 |
10.0 |
0 |
14 |
3.734 |
0.747 |
1.541 |
3.00 |
NO |
-0.988 |
0.207 |
| Controle |
M |
|
|
|
score.CI.pre |
25 |
9.360 |
10.0 |
0 |
14 |
3.353 |
0.671 |
1.384 |
5.00 |
NO |
-0.836 |
0.282 |
| Experimental |
F |
|
|
|
score.CI.pre |
19 |
8.895 |
9.0 |
0 |
14 |
3.943 |
0.904 |
1.900 |
6.00 |
YES |
-0.276 |
-0.787 |
| Experimental |
M |
|
|
|
score.CI.pre |
23 |
8.478 |
9.0 |
0 |
13 |
3.146 |
0.656 |
1.360 |
3.00 |
NO |
-1.078 |
1.179 |
| Controle |
F |
|
|
|
score.CI.pos |
25 |
7.960 |
8.0 |
0 |
17 |
4.198 |
0.840 |
1.733 |
3.00 |
YES |
0.147 |
-0.129 |
| Controle |
M |
|
|
|
score.CI.pos |
25 |
8.400 |
9.0 |
0 |
16 |
4.856 |
0.971 |
2.005 |
6.00 |
YES |
-0.433 |
-0.973 |
| Experimental |
F |
|
|
|
score.CI.pos |
19 |
8.947 |
9.0 |
0 |
17 |
4.588 |
1.053 |
2.211 |
4.50 |
NO |
-0.504 |
-0.482 |
| Experimental |
M |
|
|
|
score.CI.pos |
23 |
10.217 |
11.0 |
0 |
15 |
4.022 |
0.839 |
1.739 |
5.50 |
NO |
-0.954 |
0.044 |
| Controle |
|
Rural |
|
|
score.CI.pre |
12 |
10.417 |
10.0 |
6 |
14 |
2.234 |
0.645 |
1.420 |
1.75 |
YES |
-0.168 |
-0.750 |
| Controle |
|
Urbana |
|
|
score.CI.pre |
26 |
8.923 |
10.0 |
0 |
14 |
4.137 |
0.811 |
1.671 |
5.25 |
NO |
-0.922 |
-0.305 |
| Controle |
|
|
|
|
score.CI.pre |
12 |
9.000 |
8.5 |
4 |
13 |
3.045 |
0.879 |
1.935 |
4.75 |
YES |
0.035 |
-1.459 |
| Experimental |
|
Rural |
|
|
score.CI.pre |
15 |
9.467 |
9.0 |
5 |
14 |
3.044 |
0.786 |
1.686 |
4.00 |
YES |
0.151 |
-1.295 |
| Experimental |
|
Urbana |
|
|
score.CI.pre |
16 |
7.438 |
8.5 |
0 |
13 |
3.521 |
0.880 |
1.876 |
4.00 |
NO |
-0.848 |
-0.026 |
| Experimental |
|
|
|
|
score.CI.pre |
11 |
9.364 |
9.0 |
1 |
14 |
3.802 |
1.146 |
2.554 |
5.50 |
NO |
-0.672 |
-0.453 |
| Controle |
|
Rural |
|
|
score.CI.pos |
12 |
8.417 |
9.0 |
0 |
12 |
3.476 |
1.003 |
2.209 |
2.50 |
NO |
-1.071 |
0.368 |
| Controle |
|
Urbana |
|
|
score.CI.pos |
26 |
8.154 |
7.5 |
0 |
17 |
4.905 |
0.962 |
1.981 |
6.75 |
YES |
0.048 |
-0.914 |
| Controle |
|
|
|
|
score.CI.pos |
12 |
8.000 |
8.0 |
0 |
15 |
4.824 |
1.393 |
3.065 |
5.25 |
YES |
-0.289 |
-1.109 |
| Experimental |
|
Rural |
|
|
score.CI.pos |
15 |
8.933 |
9.0 |
0 |
17 |
4.682 |
1.209 |
2.593 |
6.50 |
YES |
-0.128 |
-0.924 |
| Experimental |
|
Urbana |
|
|
score.CI.pos |
16 |
9.375 |
10.5 |
0 |
14 |
3.914 |
0.978 |
2.085 |
2.50 |
NO |
-1.127 |
0.179 |
| Experimental |
|
|
|
|
score.CI.pos |
11 |
11.000 |
12.0 |
0 |
15 |
4.313 |
1.300 |
2.897 |
4.00 |
NO |
-1.353 |
1.038 |
| Controle |
|
|
Rural |
|
score.CI.pre |
14 |
8.214 |
8.5 |
0 |
14 |
4.406 |
1.178 |
2.544 |
5.75 |
YES |
-0.444 |
-1.091 |
| Controle |
|
|
Urbana |
|
score.CI.pre |
36 |
9.722 |
10.0 |
0 |
14 |
3.067 |
0.511 |
1.038 |
3.00 |
NO |
-1.091 |
1.223 |
| Experimental |
|
|
Rural |
|
score.CI.pre |
13 |
9.077 |
10.0 |
1 |
14 |
3.730 |
1.034 |
2.254 |
4.00 |
NO |
-0.563 |
-0.709 |
| Experimental |
|
|
Urbana |
|
score.CI.pre |
29 |
8.483 |
9.0 |
0 |
14 |
3.429 |
0.637 |
1.304 |
3.00 |
NO |
-0.573 |
0.523 |
| Controle |
|
|
Rural |
|
score.CI.pos |
14 |
9.143 |
9.5 |
0 |
17 |
5.763 |
1.540 |
3.327 |
8.75 |
YES |
-0.315 |
-1.363 |
| Controle |
|
|
Urbana |
|
score.CI.pos |
36 |
7.806 |
8.0 |
0 |
16 |
3.934 |
0.656 |
1.331 |
5.25 |
YES |
-0.311 |
-0.272 |
| Experimental |
|
|
Rural |
|
score.CI.pos |
13 |
9.308 |
10.0 |
0 |
17 |
5.313 |
1.474 |
3.211 |
6.00 |
YES |
-0.432 |
-1.020 |
| Experimental |
|
|
Urbana |
|
score.CI.pos |
29 |
9.793 |
10.0 |
0 |
15 |
3.830 |
0.711 |
1.457 |
4.00 |
NO |
-0.941 |
0.143 |
| Controle |
|
|
|
1st quintile |
score.CI.pre |
4 |
1.000 |
0.5 |
0 |
3 |
1.414 |
0.707 |
2.250 |
1.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.CI.pre |
8 |
5.750 |
6.0 |
4 |
7 |
0.886 |
0.313 |
0.741 |
0.25 |
NO |
-0.673 |
-0.539 |
| Controle |
|
|
|
3rd quintile |
score.CI.pre |
28 |
10.071 |
10.0 |
8 |
12 |
1.184 |
0.224 |
0.459 |
2.00 |
YES |
-0.003 |
-0.804 |
| Controle |
|
|
|
4th quintile |
score.CI.pre |
10 |
13.300 |
13.0 |
13 |
14 |
0.483 |
0.153 |
0.346 |
0.75 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.CI.pre |
3 |
0.333 |
0.0 |
0 |
1 |
0.577 |
0.333 |
1.434 |
0.50 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.CI.pre |
13 |
6.385 |
7.0 |
5 |
7 |
0.870 |
0.241 |
0.526 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.CI.pre |
19 |
9.737 |
10.0 |
8 |
12 |
1.098 |
0.252 |
0.529 |
1.50 |
YES |
0.257 |
-0.944 |
| Experimental |
|
|
|
4th quintile |
score.CI.pre |
7 |
13.571 |
14.0 |
13 |
14 |
0.535 |
0.202 |
0.494 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.CI.pos |
4 |
8.000 |
7.0 |
2 |
16 |
5.888 |
2.944 |
9.369 |
5.00 |
YES |
0.353 |
-1.875 |
| Controle |
|
|
|
2nd quintile |
score.CI.pos |
8 |
6.750 |
6.5 |
0 |
15 |
4.400 |
1.556 |
3.678 |
3.00 |
YES |
0.384 |
-0.703 |
| Controle |
|
|
|
3rd quintile |
score.CI.pos |
28 |
8.107 |
8.0 |
0 |
16 |
3.881 |
0.733 |
1.505 |
4.25 |
YES |
-0.500 |
-0.023 |
| Controle |
|
|
|
4th quintile |
score.CI.pos |
10 |
9.600 |
12.0 |
0 |
17 |
5.854 |
1.851 |
4.188 |
6.75 |
NO |
-0.598 |
-1.211 |
| Experimental |
|
|
|
1st quintile |
score.CI.pos |
3 |
4.333 |
0.0 |
0 |
13 |
7.506 |
4.333 |
18.645 |
6.50 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.CI.pos |
13 |
8.308 |
9.0 |
0 |
13 |
3.816 |
1.058 |
2.306 |
4.00 |
NO |
-0.800 |
-0.303 |
| Experimental |
|
|
|
3rd quintile |
score.CI.pos |
19 |
10.632 |
11.0 |
2 |
17 |
3.774 |
0.866 |
1.819 |
4.00 |
YES |
-0.463 |
-0.457 |
| Experimental |
|
|
|
4th quintile |
score.CI.pos |
7 |
11.714 |
12.0 |
6 |
14 |
2.928 |
1.107 |
2.708 |
3.00 |
NO |
-0.887 |
-0.751 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "score.CI.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.CI"]] <- c(pdat[["score.CI.pre"]], pdat[["score.CI.pos"]])
aov = anova_test(pdat, score.CI.pos ~ score.CI.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, score.CI.pos ~ grupo, covariate = score.CI.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "score.CI.pos", "grupo", covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.CI.pre","se.score.CI.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(score.CI.pos ~ score.CI.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.CI"]] <- c(wdat[["score.CI.pre"]], wdat[["score.CI.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## [1] "P294"
aov = anova_test(wdat, score.CI.pos ~ score.CI.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 score.CI.pre 1 88 9.470 0.003 * 0.097
## 2 grupo 1 88 3.101 0.082 0.034
| score.CI.pre |
1 |
88 |
9.470 |
0.003 |
* |
0.097 |
| grupo |
1 |
88 |
3.101 |
0.082 |
|
0.034 |
pwc <- emmeans_test(wdat, score.CI.pos ~ grupo, covariate = score.CI.pre,
p.adjust.method = "bonferroni")
| score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
88 |
-1.761 |
0.082 |
0.082 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
score.CI |
pre |
pos |
178 |
1.202 |
0.231 |
0.231 |
ns |
| Experimental |
time |
score.CI |
pre |
pos |
178 |
-1.132 |
0.259 |
0.259 |
ns |
ds <- get.descriptives(wdat, "score.CI.pos", "grupo", covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.CI.pre","se.score.CI.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
49 |
9.306 |
0.507 |
8.347 |
0.626 |
8.234 |
0.593 |
7.055 |
9.413 |
| Experimental |
42 |
8.667 |
0.538 |
9.643 |
0.661 |
9.775 |
0.641 |
8.501 |
11.049 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "score.CI.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "score.CI.pos", "grupo", aov, pwc, covar = "score.CI.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Irregular Correct Words") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "score.CI", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(score.CI.pos ~ score.CI.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.974 0.0641
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 89 0.158 0.692
ANCOVA and
Pairwise for two factors grupo:genero
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["genero"]]),],
"score.CI.pos", c("grupo","genero"))
pdat = pdat[pdat[["genero"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["genero"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["genero"]] = factor(
pdat[["genero"]],
level[["genero"]][level[["genero"]] %in% unique(pdat[["genero"]])])
pdat.long <- rbind(pdat[,c("id","grupo","genero")], pdat[,c("id","grupo","genero")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.CI"]] <- c(pdat[["score.CI.pre"]], pdat[["score.CI.pos"]])
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(pdat, score.CI.pos ~ score.CI.pre + grupo*genero)
laov[["grupo:genero"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(pdat, grupo), score.CI.pos ~ genero,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, genero), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","genero")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(pdat, "score.CI.pos", c("grupo","genero"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.CI.pre","se.score.CI.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["genero"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.CI.pos ~ score.CI.pre + grupo*genero, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","genero")], wdat[,c("id","grupo","genero")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.CI"]] <- c(wdat[["score.CI.pre"]], wdat[["score.CI.pos"]])
ldat[["grupo:genero"]] = wdat
(non.normal)
}
## [1] "P300" "P282"
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(wdat, score.CI.pos ~ score.CI.pre + grupo*genero)
laov[["grupo:genero"]] <- merge(get_anova_table(aov), laov[["grupo:genero"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.CI.pre |
1 |
85 |
10.874 |
0.001 |
* |
0.113 |
| grupo |
1 |
85 |
2.204 |
0.141 |
|
0.025 |
| genero |
1 |
85 |
2.240 |
0.138 |
|
0.026 |
| grupo:genero |
1 |
85 |
0.029 |
0.865 |
|
0.000 |
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(wdat, grupo), score.CI.pos ~ genero,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, genero), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
|
F |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
85 |
-0.914 |
0.363 |
0.363 |
ns |
|
M |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
85 |
-1.185 |
0.239 |
0.239 |
ns |
| Controle |
|
score.CI.pre*genero |
score.CI.pos |
F |
M |
85 |
-0.979 |
0.330 |
0.330 |
ns |
| Experimental |
|
score.CI.pre*genero |
score.CI.pos |
F |
M |
85 |
-1.145 |
0.256 |
0.256 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","genero")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:genero"]],
by=c("grupo","genero","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
score.CI |
pre |
pos |
172 |
1.148 |
0.252 |
0.252 |
ns |
| Controle |
M |
time |
score.CI |
pre |
pos |
172 |
0.150 |
0.881 |
0.881 |
ns |
| Experimental |
F |
time |
score.CI |
pre |
pos |
172 |
-0.041 |
0.967 |
0.967 |
ns |
| Experimental |
M |
time |
score.CI |
pre |
pos |
172 |
-1.497 |
0.136 |
0.136 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(wdat, "score.CI.pos", c("grupo","genero"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.CI.pre","se.score.CI.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- merge(ds, lemms[["grupo:genero"]],
by=c("grupo","genero"), suffixes = c("","'"))
}
| Controle |
F |
25 |
9.240 |
0.747 |
7.960 |
0.840 |
7.859 |
0.810 |
6.248 |
9.470 |
| Controle |
M |
23 |
9.304 |
0.729 |
9.130 |
0.903 |
9.004 |
0.845 |
7.324 |
10.683 |
| Experimental |
F |
19 |
8.895 |
0.904 |
8.947 |
1.053 |
8.985 |
0.929 |
7.139 |
10.831 |
| Experimental |
M |
23 |
8.478 |
0.656 |
10.217 |
0.839 |
10.423 |
0.846 |
8.740 |
12.105 |
Plots for ancova
if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "genero", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "genero", "grupo", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.CI.pos", c("grupo","genero"), aov, pwcs, covar = "score.CI.pre",
theme = "classic", color = color[["grupo:genero"]],
subtitle = which(aov$Effect == "grupo:genero"))
}
if (length(unique(pdat[["genero"]])) >= 2) {
plots[["grupo:genero"]] + ggplot2::ylab("Reading Irregular Correct Words") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.CI", c("grupo","genero"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["genero"]])) >= 2)
plots[["grupo:genero"]] + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
facet.by = c("grupo","genero"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "grupo", facet.by = "genero", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "genero", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = genero)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["genero"]])) >= 2)
res <- augment(lm(score.CI.pos ~ score.CI.pre + grupo*genero, data = wdat))
if (length(unique(pdat[["genero"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.977 0.103
if (length(unique(pdat[["genero"]])) >= 2)
levene_test(res, .resid ~ grupo*genero)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 86 0.0814 0.970
ANCOVA
and Pairwise for two factors
grupo:zona.participante
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.participante"]]),],
"score.CI.pos", c("grupo","zona.participante"))
pdat = pdat[pdat[["zona.participante"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.participante"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.participante"]] = factor(
pdat[["zona.participante"]],
level[["zona.participante"]][level[["zona.participante"]] %in% unique(pdat[["zona.participante"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.participante")], pdat[,c("id","grupo","zona.participante")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.CI"]] <- c(pdat[["score.CI.pre"]], pdat[["score.CI.pos"]])
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(pdat, score.CI.pos ~ score.CI.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(pdat, grupo), score.CI.pos ~ zona.participante,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.participante), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.participante")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(pdat, "score.CI.pos", c("grupo","zona.participante"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.CI.pre","se.score.CI.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.participante"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.CI.pos ~ score.CI.pre + grupo*zona.participante, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.participante")], wdat[,c("id","grupo","zona.participante")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.CI"]] <- c(wdat[["score.CI.pre"]], wdat[["score.CI.pos"]])
ldat[["grupo:zona.participante"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(wdat, score.CI.pos ~ score.CI.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- merge(get_anova_table(aov), laov[["grupo:zona.participante"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.CI.pre |
1 |
64 |
4.421 |
0.039 |
* |
0.065 |
| grupo |
1 |
64 |
1.555 |
0.217 |
|
0.024 |
| zona.participante |
1 |
64 |
0.329 |
0.568 |
|
0.005 |
| grupo:zona.participante |
1 |
64 |
0.164 |
0.686 |
|
0.003 |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(wdat, grupo), score.CI.pos ~ zona.participante,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.participante), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
|
Rural |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
64 |
-0.490 |
0.626 |
0.626 |
ns |
|
Urbana |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
64 |
-1.223 |
0.226 |
0.226 |
ns |
| Controle |
|
score.CI.pre*zona.participante |
score.CI.pos |
Rural |
Urbana |
64 |
-0.142 |
0.887 |
0.887 |
ns |
| Experimental |
|
score.CI.pre*zona.participante |
score.CI.pos |
Rural |
Urbana |
64 |
-0.692 |
0.492 |
0.492 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.participante")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.participante"]],
by=c("grupo","zona.participante","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.CI |
pre |
pos |
130 |
1.228 |
0.222 |
0.222 |
ns |
| Controle |
Urbana |
time |
score.CI |
pre |
pos |
130 |
0.695 |
0.488 |
0.488 |
ns |
| Experimental |
Rural |
time |
score.CI |
pre |
pos |
130 |
0.366 |
0.715 |
0.715 |
ns |
| Experimental |
Urbana |
time |
score.CI |
pre |
pos |
130 |
-1.374 |
0.172 |
0.172 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(wdat, "score.CI.pos", c("grupo","zona.participante"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.CI.pre","se.score.CI.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- merge(ds, lemms[["grupo:zona.participante"]],
by=c("grupo","zona.participante"), suffixes = c("","'"))
}
| Controle |
Rural |
12 |
10.417 |
0.645 |
8.417 |
1.003 |
7.948 |
1.264 |
5.423 |
10.473 |
| Controle |
Urbana |
26 |
8.923 |
0.811 |
8.154 |
0.962 |
8.165 |
0.845 |
6.476 |
9.853 |
| Experimental |
Rural |
15 |
9.467 |
0.786 |
8.933 |
1.209 |
8.770 |
1.116 |
6.541 |
10.998 |
| Experimental |
Urbana |
16 |
7.438 |
0.880 |
9.375 |
0.978 |
9.862 |
1.102 |
7.660 |
12.064 |
Plots for ancova
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.participante", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.participante", "grupo", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.CI.pos", c("grupo","zona.participante"), aov, pwcs, covar = "score.CI.pre",
theme = "classic", color = color[["grupo:zona.participante"]],
subtitle = which(aov$Effect == "grupo:zona.participante"))
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots[["grupo:zona.participante"]] + ggplot2::ylab("Reading Irregular Correct Words") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.CI", c("grupo","zona.participante"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.participante"]])) >= 2)
plots[["grupo:zona.participante"]] + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
facet.by = c("grupo","zona.participante"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "grupo", facet.by = "zona.participante", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "zona.participante", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.participante)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.participante"]])) >= 2)
res <- augment(lm(score.CI.pos ~ score.CI.pre + grupo*zona.participante, data = wdat))
if (length(unique(pdat[["zona.participante"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.976 0.199
if (length(unique(pdat[["zona.participante"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.participante)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 65 0.877 0.458
ANCOVA and
Pairwise for two factors grupo:zona.escola
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.escola"]]),],
"score.CI.pos", c("grupo","zona.escola"))
pdat = pdat[pdat[["zona.escola"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.escola"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.escola"]] = factor(
pdat[["zona.escola"]],
level[["zona.escola"]][level[["zona.escola"]] %in% unique(pdat[["zona.escola"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.escola")], pdat[,c("id","grupo","zona.escola")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.CI"]] <- c(pdat[["score.CI.pre"]], pdat[["score.CI.pos"]])
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(pdat, score.CI.pos ~ score.CI.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(pdat, grupo), score.CI.pos ~ zona.escola,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.escola), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.escola")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(pdat, "score.CI.pos", c("grupo","zona.escola"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.CI.pre","se.score.CI.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.escola"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.CI.pos ~ score.CI.pre + grupo*zona.escola, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.escola")], wdat[,c("id","grupo","zona.escola")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.CI"]] <- c(wdat[["score.CI.pre"]], wdat[["score.CI.pos"]])
ldat[["grupo:zona.escola"]] = wdat
(non.normal)
}
## [1] "P294" "P300" "P83"
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(wdat, score.CI.pos ~ score.CI.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- merge(get_anova_table(aov), laov[["grupo:zona.escola"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.CI.pre |
1 |
84 |
13.441 |
0.000 |
* |
0.138 |
| grupo |
1 |
84 |
1.931 |
0.168 |
|
0.022 |
| zona.escola |
1 |
84 |
0.103 |
0.750 |
|
0.001 |
| grupo:zona.escola |
1 |
84 |
1.189 |
0.279 |
|
0.014 |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(wdat, grupo), score.CI.pos ~ zona.escola,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.escola), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
|
Rural |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
84 |
0.143 |
0.887 |
0.887 |
ns |
|
Urbana |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
84 |
-1.762 |
0.082 |
0.082 |
ns |
| Controle |
|
score.CI.pre*zona.escola |
score.CI.pos |
Rural |
Urbana |
84 |
0.989 |
0.325 |
0.325 |
ns |
| Experimental |
|
score.CI.pre*zona.escola |
score.CI.pos |
Rural |
Urbana |
84 |
-0.567 |
0.572 |
0.572 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.escola")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.escola"]],
by=c("grupo","zona.escola","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.CI |
pre |
pos |
170 |
-0.629 |
0.530 |
0.530 |
ns |
| Controle |
Urbana |
time |
score.CI |
pre |
pos |
170 |
1.167 |
0.245 |
0.245 |
ns |
| Experimental |
Rural |
time |
score.CI |
pre |
pos |
170 |
-0.151 |
0.880 |
0.880 |
ns |
| Experimental |
Urbana |
time |
score.CI |
pre |
pos |
170 |
-1.278 |
0.203 |
0.203 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(wdat, "score.CI.pos", c("grupo","zona.escola"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.CI.pre","se.score.CI.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- merge(ds, lemms[["grupo:zona.escola"]],
by=c("grupo","zona.escola"), suffixes = c("","'"))
}
| Controle |
Rural |
14 |
8.214 |
1.178 |
9.143 |
1.540 |
9.472 |
1.061 |
7.363 |
11.581 |
| Controle |
Urbana |
33 |
9.636 |
0.548 |
8.515 |
0.569 |
8.213 |
0.693 |
6.834 |
9.591 |
| Experimental |
Rural |
13 |
9.077 |
1.034 |
9.308 |
1.474 |
9.254 |
1.097 |
7.073 |
11.435 |
| Experimental |
Urbana |
29 |
8.483 |
0.637 |
9.793 |
0.711 |
10.003 |
0.736 |
8.538 |
11.467 |
Plots for ancova
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.escola", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.escola", "grupo", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.CI.pos", c("grupo","zona.escola"), aov, pwcs, covar = "score.CI.pre",
theme = "classic", color = color[["grupo:zona.escola"]],
subtitle = which(aov$Effect == "grupo:zona.escola"))
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots[["grupo:zona.escola"]] + ggplot2::ylab("Reading Irregular Correct Words") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.CI", c("grupo","zona.escola"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.escola"]])) >= 2)
plots[["grupo:zona.escola"]] + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
facet.by = c("grupo","zona.escola"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "grupo", facet.by = "zona.escola", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "zona.escola", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.escola)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.escola"]])) >= 2)
res <- augment(lm(score.CI.pos ~ score.CI.pre + grupo*zona.escola, data = wdat))
if (length(unique(pdat[["zona.escola"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.979 0.161
if (length(unique(pdat[["zona.escola"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.escola)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 85 3.20 0.0275
ANCOVA
and Pairwise for two factors
grupo:score.CI.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["score.CI.quintile"]]),],
"score.CI.pos", c("grupo","score.CI.quintile"))
pdat = pdat[pdat[["score.CI.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["score.CI.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["score.CI.quintile"]] = factor(
pdat[["score.CI.quintile"]],
level[["score.CI.quintile"]][level[["score.CI.quintile"]] %in% unique(pdat[["score.CI.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","score.CI.quintile")], pdat[,c("id","grupo","score.CI.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.CI"]] <- c(pdat[["score.CI.pre"]], pdat[["score.CI.pos"]])
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
aov = anova_test(pdat, score.CI.pos ~ score.CI.pre + grupo*score.CI.quintile)
laov[["grupo:score.CI.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.CI.quintile"]] <- emmeans_test(
group_by(pdat, grupo), score.CI.pos ~ score.CI.quintile,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, score.CI.quintile), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.CI.quintile"]])
pwc <- pwc[,c("grupo","score.CI.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.CI.quintile")])]
}
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","score.CI.quintile")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.CI.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "score.CI.pos", c("grupo","score.CI.quintile"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.CI.quintile"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.CI.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.CI.quintile","n","mean.score.CI.pre","se.score.CI.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.CI.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.CI.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.CI.pos ~ score.CI.pre + grupo*score.CI.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","score.CI.quintile")], wdat[,c("id","grupo","score.CI.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.CI"]] <- c(wdat[["score.CI.pre"]], wdat[["score.CI.pos"]])
ldat[["grupo:score.CI.quintile"]] = wdat
(non.normal)
}
## [1] "P300" "P85" "P83" "P256" "P305"
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
aov = anova_test(wdat, score.CI.pos ~ score.CI.pre + grupo*score.CI.quintile)
laov[["grupo:score.CI.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:score.CI.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.CI.pre |
1 |
73 |
4.573 |
0.036 |
* |
0.059 |
| grupo |
1 |
73 |
6.287 |
0.014 |
* |
0.079 |
| score.CI.quintile |
2 |
73 |
0.307 |
0.736 |
|
0.008 |
| grupo:score.CI.quintile |
2 |
73 |
0.958 |
0.389 |
|
0.026 |
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.CI.quintile"]] <- emmeans_test(
group_by(wdat, grupo), score.CI.pos ~ score.CI.quintile,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, score.CI.quintile), score.CI.pos ~ grupo,
covariate = score.CI.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.CI.quintile"]])
pwc <- pwc[,c("grupo","score.CI.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.CI.quintile")])]
}
|
2nd quintile |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
73 |
-0.357 |
0.722 |
0.722 |
ns |
|
3rd quintile |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
73 |
-2.789 |
0.007 |
0.007 |
** |
|
4th quintile |
score.CI.pre*grupo |
score.CI.pos |
Controle |
Experimental |
73 |
-0.475 |
0.636 |
0.636 |
ns |
| Controle |
|
score.CI.pre*score.CI.quintile |
score.CI.pos |
2nd quintile |
3rd quintile |
73 |
1.332 |
0.187 |
0.561 |
ns |
| Controle |
|
score.CI.pre*score.CI.quintile |
score.CI.pos |
2nd quintile |
4th quintile |
73 |
1.022 |
0.310 |
0.930 |
ns |
| Controle |
|
score.CI.pre*score.CI.quintile |
score.CI.pos |
3rd quintile |
4th quintile |
73 |
0.295 |
0.769 |
1.000 |
ns |
| Experimental |
|
score.CI.pre*score.CI.quintile |
score.CI.pos |
2nd quintile |
3rd quintile |
73 |
0.364 |
0.717 |
1.000 |
ns |
| Experimental |
|
score.CI.pre*score.CI.quintile |
score.CI.pos |
2nd quintile |
4th quintile |
73 |
1.026 |
0.308 |
0.925 |
ns |
| Experimental |
|
score.CI.pre*score.CI.quintile |
score.CI.pos |
3rd quintile |
4th quintile |
73 |
1.234 |
0.221 |
0.664 |
ns |
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","score.CI.quintile")),
score.CI ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.CI.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:score.CI.quintile"]],
by=c("grupo","score.CI.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
2nd quintile |
time |
score.CI |
pre |
pos |
148 |
-1.416 |
0.159 |
0.159 |
ns |
| Controle |
3rd quintile |
time |
score.CI |
pre |
pos |
148 |
2.318 |
0.022 |
0.022 |
* |
| Controle |
4th quintile |
time |
score.CI |
pre |
pos |
148 |
2.141 |
0.034 |
0.034 |
* |
| Experimental |
2nd quintile |
time |
score.CI |
pre |
pos |
148 |
-2.318 |
0.022 |
0.022 |
* |
| Experimental |
3rd quintile |
time |
score.CI |
pre |
pos |
148 |
-1.577 |
0.117 |
0.117 |
ns |
| Experimental |
4th quintile |
time |
score.CI |
pre |
pos |
148 |
1.315 |
0.190 |
0.190 |
ns |
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "score.CI.pos", c("grupo","score.CI.quintile"), covar = "score.CI.pre")
ds <- merge(ds[ds$variable != "score.CI.pre",],
ds[ds$variable == "score.CI.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.CI.quintile"), all.x = T, suffixes = c("", ".score.CI.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.CI.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.CI.quintile","n","mean.score.CI.pre","se.score.CI.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.CI.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.CI.quintile"]] <- merge(ds, lemms[["grupo:score.CI.quintile"]],
by=c("grupo","score.CI.quintile"), suffixes = c("","'"))
}
| Controle |
2nd quintile |
7 |
5.714 |
0.360 |
7.714 |
1.409 |
11.201 |
2.103 |
7.010 |
15.391 |
| Controle |
3rd quintile |
27 |
10.074 |
0.232 |
8.407 |
0.694 |
8.127 |
0.689 |
6.755 |
9.500 |
| Controle |
4th quintile |
9 |
13.333 |
0.167 |
10.667 |
1.691 |
7.571 |
1.862 |
3.861 |
11.282 |
| Experimental |
2nd quintile |
12 |
6.500 |
0.230 |
9.000 |
0.870 |
11.808 |
1.659 |
8.501 |
15.114 |
| Experimental |
3rd quintile |
18 |
9.722 |
0.266 |
11.111 |
0.762 |
11.135 |
0.828 |
9.485 |
12.785 |
| Experimental |
4th quintile |
7 |
13.571 |
0.202 |
11.714 |
1.107 |
8.413 |
2.036 |
4.355 |
12.471 |
Plots for ancova
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "score.CI.quintile", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:score.CI.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["score.CI.quintile"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "score.CI.quintile", "grupo", aov, ylab = "Reading Irregular Correct Words",
subtitle = which(aov$Effect == "grupo:score.CI.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.CI.pos", c("grupo","score.CI.quintile"), aov, pwcs, covar = "score.CI.pre",
theme = "classic", color = color[["grupo:score.CI.quintile"]],
subtitle = which(aov$Effect == "grupo:score.CI.quintile"))
}
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
plots[["grupo:score.CI.quintile"]] + ggplot2::ylab("Reading Irregular Correct Words") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.CI", c("grupo","score.CI.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["score.CI.quintile"]])) >= 2)
plots[["grupo:score.CI.quintile"]] + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
facet.by = c("grupo","score.CI.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "grupo", facet.by = "score.CI.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.CI.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.CI.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.CI.pre", y = "score.CI.pos", size = 0.5,
color = "score.CI.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = score.CI.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.CI.quintile"))) +
ggplot2::scale_color_manual(values = color[["score.CI.quintile"]]) +
ggplot2::ylab("Reading Irregular Correct Words") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["score.CI.quintile"]])) >= 2)
res <- augment(lm(score.CI.pos ~ score.CI.pre + grupo*score.CI.quintile, data = wdat))
if (length(unique(pdat[["score.CI.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.971 0.0676
if (length(unique(pdat[["score.CI.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*score.CI.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 5 74 0.319 0.900
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.CI.pre |
49 |
9.306 |
10.0 |
0 |
14 |
3.548 |
0.507 |
1.019 |
4.00 |
NO |
-0.957 |
0.363 |
| Experimental |
|
|
|
|
score.CI.pre |
42 |
8.667 |
9.0 |
0 |
14 |
3.490 |
0.538 |
1.088 |
4.00 |
NO |
-0.574 |
0.210 |
|
|
|
|
|
score.CI.pre |
91 |
9.011 |
10.0 |
0 |
14 |
3.517 |
0.369 |
0.732 |
4.00 |
NO |
-0.784 |
0.286 |
| Controle |
|
|
|
|
score.CI.pos |
49 |
8.347 |
8.0 |
0 |
17 |
4.385 |
0.626 |
1.260 |
6.00 |
YES |
-0.191 |
-0.499 |
| Experimental |
|
|
|
|
score.CI.pos |
42 |
9.643 |
10.0 |
0 |
17 |
4.282 |
0.661 |
1.334 |
5.75 |
NO |
-0.768 |
-0.122 |
|
|
|
|
|
score.CI.pos |
91 |
8.945 |
9.0 |
0 |
17 |
4.362 |
0.457 |
0.909 |
5.50 |
YES |
-0.450 |
-0.422 |
| Controle |
F |
|
|
|
score.CI.pre |
25 |
9.240 |
10.0 |
0 |
14 |
3.734 |
0.747 |
1.541 |
3.00 |
NO |
-0.988 |
0.207 |
| Controle |
M |
|
|
|
score.CI.pre |
23 |
9.304 |
10.0 |
0 |
14 |
3.496 |
0.729 |
1.512 |
5.50 |
NO |
-0.756 |
-0.033 |
| Experimental |
F |
|
|
|
score.CI.pre |
19 |
8.895 |
9.0 |
0 |
14 |
3.943 |
0.904 |
1.900 |
6.00 |
YES |
-0.276 |
-0.787 |
| Experimental |
M |
|
|
|
score.CI.pre |
23 |
8.478 |
9.0 |
0 |
13 |
3.146 |
0.656 |
1.360 |
3.00 |
NO |
-1.078 |
1.179 |
| Controle |
F |
|
|
|
score.CI.pos |
25 |
7.960 |
8.0 |
0 |
17 |
4.198 |
0.840 |
1.733 |
3.00 |
YES |
0.147 |
-0.129 |
| Controle |
M |
|
|
|
score.CI.pos |
23 |
9.130 |
10.0 |
0 |
16 |
4.331 |
0.903 |
1.873 |
5.50 |
NO |
-0.518 |
-0.561 |
| Experimental |
F |
|
|
|
score.CI.pos |
19 |
8.947 |
9.0 |
0 |
17 |
4.588 |
1.053 |
2.211 |
4.50 |
NO |
-0.504 |
-0.482 |
| Experimental |
M |
|
|
|
score.CI.pos |
23 |
10.217 |
11.0 |
0 |
15 |
4.022 |
0.839 |
1.739 |
5.50 |
NO |
-0.954 |
0.044 |
| Controle |
|
Rural |
|
|
score.CI.pre |
12 |
10.417 |
10.0 |
6 |
14 |
2.234 |
0.645 |
1.420 |
1.75 |
YES |
-0.168 |
-0.750 |
| Controle |
|
Urbana |
|
|
score.CI.pre |
26 |
8.923 |
10.0 |
0 |
14 |
4.137 |
0.811 |
1.671 |
5.25 |
NO |
-0.922 |
-0.305 |
| Experimental |
|
Rural |
|
|
score.CI.pre |
15 |
9.467 |
9.0 |
5 |
14 |
3.044 |
0.786 |
1.686 |
4.00 |
YES |
0.151 |
-1.295 |
| Experimental |
|
Urbana |
|
|
score.CI.pre |
16 |
7.438 |
8.5 |
0 |
13 |
3.521 |
0.880 |
1.876 |
4.00 |
NO |
-0.848 |
-0.026 |
| Controle |
|
Rural |
|
|
score.CI.pos |
12 |
8.417 |
9.0 |
0 |
12 |
3.476 |
1.003 |
2.209 |
2.50 |
NO |
-1.071 |
0.368 |
| Controle |
|
Urbana |
|
|
score.CI.pos |
26 |
8.154 |
7.5 |
0 |
17 |
4.905 |
0.962 |
1.981 |
6.75 |
YES |
0.048 |
-0.914 |
| Experimental |
|
Rural |
|
|
score.CI.pos |
15 |
8.933 |
9.0 |
0 |
17 |
4.682 |
1.209 |
2.593 |
6.50 |
YES |
-0.128 |
-0.924 |
| Experimental |
|
Urbana |
|
|
score.CI.pos |
16 |
9.375 |
10.5 |
0 |
14 |
3.914 |
0.978 |
2.085 |
2.50 |
NO |
-1.127 |
0.179 |
| Controle |
|
|
Rural |
|
score.CI.pre |
14 |
8.214 |
8.5 |
0 |
14 |
4.406 |
1.178 |
2.544 |
5.75 |
YES |
-0.444 |
-1.091 |
| Controle |
|
|
Urbana |
|
score.CI.pre |
33 |
9.636 |
10.0 |
0 |
14 |
3.151 |
0.548 |
1.117 |
3.00 |
NO |
-1.052 |
0.983 |
| Experimental |
|
|
Rural |
|
score.CI.pre |
13 |
9.077 |
10.0 |
1 |
14 |
3.730 |
1.034 |
2.254 |
4.00 |
NO |
-0.563 |
-0.709 |
| Experimental |
|
|
Urbana |
|
score.CI.pre |
29 |
8.483 |
9.0 |
0 |
14 |
3.429 |
0.637 |
1.304 |
3.00 |
NO |
-0.573 |
0.523 |
| Controle |
|
|
Rural |
|
score.CI.pos |
14 |
9.143 |
9.5 |
0 |
17 |
5.763 |
1.540 |
3.327 |
8.75 |
YES |
-0.315 |
-1.363 |
| Controle |
|
|
Urbana |
|
score.CI.pos |
33 |
8.515 |
8.0 |
0 |
16 |
3.270 |
0.569 |
1.160 |
6.00 |
YES |
0.005 |
0.000 |
| Experimental |
|
|
Rural |
|
score.CI.pos |
13 |
9.308 |
10.0 |
0 |
17 |
5.313 |
1.474 |
3.211 |
6.00 |
YES |
-0.432 |
-1.020 |
| Experimental |
|
|
Urbana |
|
score.CI.pos |
29 |
9.793 |
10.0 |
0 |
15 |
3.830 |
0.711 |
1.457 |
4.00 |
NO |
-0.941 |
0.143 |
| Controle |
|
|
|
2nd quintile |
score.CI.pre |
7 |
5.714 |
6.0 |
4 |
7 |
0.951 |
0.360 |
0.880 |
0.50 |
NO |
-0.528 |
-0.966 |
| Controle |
|
|
|
3rd quintile |
score.CI.pre |
27 |
10.074 |
10.0 |
8 |
12 |
1.207 |
0.232 |
0.477 |
2.00 |
YES |
-0.009 |
-0.888 |
| Controle |
|
|
|
4th quintile |
score.CI.pre |
9 |
13.333 |
13.0 |
13 |
14 |
0.500 |
0.167 |
0.384 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.CI.pre |
12 |
6.500 |
7.0 |
5 |
7 |
0.798 |
0.230 |
0.507 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.CI.pre |
18 |
9.722 |
9.5 |
8 |
12 |
1.127 |
0.266 |
0.561 |
1.75 |
YES |
0.287 |
-1.037 |
| Experimental |
|
|
|
4th quintile |
score.CI.pre |
7 |
13.571 |
14.0 |
13 |
14 |
0.535 |
0.202 |
0.494 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.CI.pos |
7 |
7.714 |
7.0 |
4 |
15 |
3.729 |
1.409 |
3.449 |
3.00 |
NO |
0.886 |
-0.710 |
| Controle |
|
|
|
3rd quintile |
score.CI.pos |
27 |
8.407 |
8.0 |
0 |
16 |
3.608 |
0.694 |
1.427 |
4.00 |
YES |
-0.466 |
0.302 |
| Controle |
|
|
|
4th quintile |
score.CI.pos |
9 |
10.667 |
12.0 |
0 |
17 |
5.074 |
1.691 |
3.901 |
5.00 |
NO |
-0.837 |
-0.437 |
| Experimental |
|
|
|
2nd quintile |
score.CI.pos |
12 |
9.000 |
9.0 |
2 |
13 |
3.015 |
0.870 |
1.916 |
3.25 |
NO |
-0.657 |
0.005 |
| Experimental |
|
|
|
3rd quintile |
score.CI.pos |
18 |
11.111 |
11.0 |
5 |
17 |
3.234 |
0.762 |
1.608 |
3.75 |
YES |
-0.143 |
-0.879 |
| Experimental |
|
|
|
4th quintile |
score.CI.pos |
7 |
11.714 |
12.0 |
6 |
14 |
2.928 |
1.107 |
2.708 |
3.00 |
NO |
-0.887 |
-0.751 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
88 |
3.101 |
0.082 |
|
0.034 |
1 |
89 |
3.721 |
0.057 |
|
0.040 |
| 2 |
score.CI.pre |
1 |
88 |
9.470 |
0.003 |
* |
0.097 |
1 |
89 |
9.283 |
0.003 |
* |
0.094 |
| 3 |
genero |
1 |
85 |
2.240 |
0.138 |
|
0.026 |
1 |
87 |
0.959 |
0.330 |
|
0.011 |
| 5 |
grupo:genero |
1 |
85 |
0.029 |
0.865 |
|
0.000 |
1 |
87 |
0.343 |
0.560 |
|
0.004 |
| 8 |
grupo:zona.participante |
1 |
64 |
0.164 |
0.686 |
|
0.003 |
1 |
64 |
0.164 |
0.686 |
|
0.003 |
| 10 |
zona.participante |
1 |
64 |
0.329 |
0.568 |
|
0.005 |
1 |
64 |
0.329 |
0.568 |
|
0.005 |
| 12 |
grupo:zona.escola |
1 |
84 |
1.189 |
0.279 |
|
0.014 |
1 |
87 |
1.922 |
0.169 |
|
0.022 |
| 14 |
zona.escola |
1 |
84 |
0.103 |
0.750 |
|
0.001 |
1 |
87 |
0.500 |
0.482 |
|
0.006 |
| 16 |
grupo:score.CI.quintile |
2 |
73 |
0.958 |
0.389 |
|
0.026 |
2 |
78 |
0.398 |
0.673 |
|
0.010 |
| 18 |
score.CI.quintile |
2 |
73 |
0.307 |
0.736 |
|
0.008 |
2 |
78 |
0.489 |
0.615 |
|
0.012 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
178 |
1.202 |
0.231 |
0.231 |
ns |
180 |
1.408 |
0.161 |
0.161 |
ns |
| Experimental |
|
|
|
|
pre |
pos |
178 |
-1.132 |
0.259 |
0.259 |
ns |
180 |
-1.125 |
0.262 |
0.262 |
ns |
|
|
|
|
|
Controle |
Experimental |
88 |
-1.761 |
0.082 |
0.082 |
ns |
89 |
-1.929 |
0.057 |
0.057 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
172 |
1.148 |
0.252 |
0.252 |
ns |
176 |
1.129 |
0.260 |
0.260 |
ns |
| Controle |
M |
|
|
|
pre |
pos |
172 |
0.150 |
0.881 |
0.881 |
ns |
176 |
0.847 |
0.398 |
0.398 |
ns |
| Controle |
|
|
|
|
F |
M |
85 |
-0.979 |
0.330 |
0.330 |
ns |
87 |
-0.329 |
0.743 |
0.743 |
ns |
| Experimental |
F |
|
|
|
pre |
pos |
172 |
-0.041 |
0.967 |
0.967 |
ns |
176 |
-0.040 |
0.968 |
0.968 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
172 |
-1.497 |
0.136 |
0.136 |
ns |
176 |
-1.472 |
0.143 |
0.143 |
ns |
| Experimental |
|
|
|
|
F |
M |
85 |
-1.145 |
0.256 |
0.256 |
ns |
87 |
-1.092 |
0.278 |
0.278 |
ns |
|
F |
|
|
|
Controle |
Experimental |
85 |
-0.914 |
0.363 |
0.363 |
ns |
87 |
-0.872 |
0.386 |
0.386 |
ns |
|
M |
|
|
|
Controle |
Experimental |
85 |
-1.185 |
0.239 |
0.239 |
ns |
87 |
-1.763 |
0.081 |
0.081 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
64 |
-0.142 |
0.887 |
0.887 |
ns |
64 |
-0.142 |
0.887 |
0.887 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
130 |
1.228 |
0.222 |
0.222 |
ns |
130 |
1.228 |
0.222 |
0.222 |
ns |
| Controle |
|
Urbana |
|
|
pre |
pos |
130 |
0.695 |
0.488 |
0.488 |
ns |
130 |
0.695 |
0.488 |
0.488 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
64 |
-0.692 |
0.492 |
0.492 |
ns |
64 |
-0.692 |
0.492 |
0.492 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
130 |
0.366 |
0.715 |
0.715 |
ns |
130 |
0.366 |
0.715 |
0.715 |
ns |
| Experimental |
|
Urbana |
|
|
pre |
pos |
130 |
-1.374 |
0.172 |
0.172 |
ns |
130 |
-1.374 |
0.172 |
0.172 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
64 |
-0.490 |
0.626 |
0.626 |
ns |
64 |
-0.490 |
0.626 |
0.626 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
64 |
-1.223 |
0.226 |
0.226 |
ns |
64 |
-1.223 |
0.226 |
0.226 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
84 |
0.989 |
0.325 |
0.325 |
ns |
87 |
1.470 |
0.145 |
0.145 |
ns |
| Controle |
|
|
Rural |
|
pre |
pos |
170 |
-0.629 |
0.530 |
0.530 |
ns |
176 |
-0.616 |
0.539 |
0.539 |
ns |
| Controle |
|
|
Urbana |
|
pre |
pos |
170 |
1.167 |
0.245 |
0.245 |
ns |
176 |
2.039 |
0.043 |
0.043 |
* |
| Experimental |
|
|
|
|
Rural |
Urbana |
84 |
-0.567 |
0.572 |
0.572 |
ns |
87 |
-0.522 |
0.603 |
0.603 |
ns |
| Experimental |
|
|
Rural |
|
pre |
pos |
170 |
-0.151 |
0.880 |
0.880 |
ns |
176 |
-0.147 |
0.883 |
0.883 |
ns |
| Experimental |
|
|
Urbana |
|
pre |
pos |
170 |
-1.278 |
0.203 |
0.203 |
ns |
176 |
-1.251 |
0.213 |
0.213 |
ns |
|
|
|
Rural |
|
Controle |
Experimental |
84 |
0.143 |
0.887 |
0.887 |
ns |
87 |
0.120 |
0.905 |
0.905 |
ns |
|
|
|
Urbana |
|
Controle |
Experimental |
84 |
-1.762 |
0.082 |
0.082 |
ns |
87 |
-2.362 |
0.020 |
0.020 |
* |
| Controle |
|
|
|
2nd quintile |
pre |
pos |
148 |
-1.416 |
0.159 |
0.159 |
ns |
158 |
-0.669 |
0.504 |
0.504 |
ns |
| Controle |
|
|
|
3rd quintile |
pre |
pos |
148 |
2.318 |
0.022 |
0.022 |
* |
158 |
2.459 |
0.015 |
0.015 |
* |
| Controle |
|
|
|
4th quintile |
pre |
pos |
148 |
2.141 |
0.034 |
0.034 |
* |
158 |
2.768 |
0.006 |
0.006 |
** |
| Controle |
|
|
|
|
2nd quintile |
3rd quintile |
73 |
1.332 |
0.187 |
0.561 |
ns |
78 |
1.152 |
0.253 |
0.758 |
ns |
| Controle |
|
|
|
|
2nd quintile |
4th quintile |
73 |
1.022 |
0.310 |
0.930 |
ns |
78 |
1.180 |
0.241 |
0.724 |
ns |
| Controle |
|
|
|
|
3rd quintile |
4th quintile |
73 |
0.295 |
0.769 |
1.000 |
ns |
78 |
0.819 |
0.415 |
1.000 |
ns |
| Experimental |
|
|
|
2nd quintile |
pre |
pos |
148 |
-2.318 |
0.022 |
0.022 |
* |
158 |
-1.641 |
0.103 |
0.103 |
ns |
| Experimental |
|
|
|
3rd quintile |
pre |
pos |
148 |
-1.577 |
0.117 |
0.117 |
ns |
158 |
-0.923 |
0.358 |
0.358 |
ns |
| Experimental |
|
|
|
4th quintile |
pre |
pos |
148 |
1.315 |
0.190 |
0.190 |
ns |
158 |
1.163 |
0.247 |
0.247 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
3rd quintile |
73 |
0.364 |
0.717 |
1.000 |
ns |
78 |
0.476 |
0.636 |
1.000 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
4th quintile |
73 |
1.026 |
0.308 |
0.925 |
ns |
78 |
0.986 |
0.327 |
0.982 |
ns |
| Experimental |
|
|
|
|
3rd quintile |
4th quintile |
73 |
1.234 |
0.221 |
0.664 |
ns |
78 |
1.093 |
0.278 |
0.834 |
ns |
|
|
|
|
2nd quintile |
Controle |
Experimental |
73 |
-0.357 |
0.722 |
0.722 |
ns |
78 |
-0.508 |
0.613 |
0.613 |
ns |
|
|
|
|
3rd quintile |
Controle |
Experimental |
73 |
-2.789 |
0.007 |
0.007 |
** |
78 |
-2.375 |
0.020 |
0.020 |
* |
|
|
|
|
4th quintile |
Controle |
Experimental |
73 |
-0.475 |
0.636 |
0.636 |
ns |
78 |
-0.931 |
0.355 |
0.355 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
49 |
9.306 |
0.507 |
8.347 |
0.626 |
8.234 |
0.593 |
7.055 |
9.413 |
50 |
9.300 |
0.497 |
8.180 |
0.636 |
8.068 |
0.597 |
6.883 |
9.254 |
-1 |
| Experimental |
|
|
|
|
42 |
8.667 |
0.538 |
9.643 |
0.661 |
9.775 |
0.641 |
8.501 |
11.049 |
42 |
8.667 |
0.538 |
9.643 |
0.661 |
9.776 |
0.651 |
8.482 |
11.070 |
0 |
| Controle |
F |
|
|
|
25 |
9.240 |
0.747 |
7.960 |
0.840 |
7.859 |
0.810 |
6.248 |
9.470 |
25 |
9.240 |
0.747 |
7.960 |
0.840 |
7.870 |
0.846 |
6.189 |
9.552 |
0 |
| Controle |
M |
|
|
|
23 |
9.304 |
0.729 |
9.130 |
0.903 |
9.004 |
0.845 |
7.324 |
10.683 |
25 |
9.360 |
0.671 |
8.400 |
0.971 |
8.263 |
0.847 |
6.580 |
9.946 |
-2 |
| Experimental |
F |
|
|
|
19 |
8.895 |
0.904 |
8.947 |
1.053 |
8.985 |
0.929 |
7.139 |
10.831 |
19 |
8.895 |
0.904 |
8.947 |
1.053 |
8.993 |
0.970 |
7.065 |
10.921 |
0 |
| Experimental |
M |
|
|
|
23 |
8.478 |
0.656 |
10.217 |
0.839 |
10.423 |
0.846 |
8.740 |
12.105 |
23 |
8.478 |
0.656 |
10.217 |
0.839 |
10.426 |
0.884 |
8.668 |
12.183 |
0 |
| Controle |
|
Rural |
|
|
12 |
10.417 |
0.645 |
8.417 |
1.003 |
7.948 |
1.264 |
5.423 |
10.473 |
12 |
10.417 |
0.645 |
8.417 |
1.003 |
7.948 |
1.264 |
5.423 |
10.473 |
0 |
| Controle |
|
Urbana |
|
|
26 |
8.923 |
0.811 |
8.154 |
0.962 |
8.165 |
0.845 |
6.476 |
9.853 |
26 |
8.923 |
0.811 |
8.154 |
0.962 |
8.165 |
0.845 |
6.476 |
9.853 |
0 |
| Experimental |
|
Rural |
|
|
15 |
9.467 |
0.786 |
8.933 |
1.209 |
8.770 |
1.116 |
6.541 |
10.998 |
15 |
9.467 |
0.786 |
8.933 |
1.209 |
8.770 |
1.116 |
6.541 |
10.998 |
0 |
| Experimental |
|
Urbana |
|
|
16 |
7.438 |
0.880 |
9.375 |
0.978 |
9.862 |
1.102 |
7.660 |
12.064 |
16 |
7.438 |
0.880 |
9.375 |
0.978 |
9.862 |
1.102 |
7.660 |
12.064 |
0 |
| Controle |
|
|
Rural |
|
14 |
8.214 |
1.178 |
9.143 |
1.540 |
9.472 |
1.061 |
7.363 |
11.581 |
14 |
8.214 |
1.178 |
9.143 |
1.540 |
9.475 |
1.127 |
7.234 |
11.716 |
0 |
| Controle |
|
|
Urbana |
|
33 |
9.636 |
0.548 |
8.515 |
0.569 |
8.213 |
0.693 |
6.834 |
9.591 |
36 |
9.722 |
0.511 |
7.806 |
0.656 |
7.509 |
0.706 |
6.106 |
8.913 |
-3 |
| Experimental |
|
|
Rural |
|
13 |
9.077 |
1.034 |
9.308 |
1.474 |
9.254 |
1.097 |
7.073 |
11.435 |
13 |
9.077 |
1.034 |
9.308 |
1.474 |
9.280 |
1.165 |
6.964 |
11.596 |
0 |
| Experimental |
|
|
Urbana |
|
29 |
8.483 |
0.637 |
9.793 |
0.711 |
10.003 |
0.736 |
8.538 |
11.467 |
29 |
8.483 |
0.637 |
9.793 |
0.711 |
10.013 |
0.783 |
8.457 |
11.570 |
0 |
| Controle |
|
|
|
2nd quintile |
7 |
5.714 |
0.360 |
7.714 |
1.409 |
11.201 |
2.103 |
7.010 |
15.391 |
8 |
5.750 |
0.313 |
6.750 |
1.556 |
10.659 |
2.287 |
6.107 |
15.212 |
-1 |
| Controle |
|
|
|
3rd quintile |
27 |
10.074 |
0.232 |
8.407 |
0.694 |
8.127 |
0.689 |
6.755 |
9.500 |
28 |
10.071 |
0.224 |
8.107 |
0.733 |
7.733 |
0.778 |
6.185 |
9.281 |
-1 |
| Controle |
|
|
|
4th quintile |
9 |
13.333 |
0.167 |
10.667 |
1.691 |
7.571 |
1.862 |
3.861 |
11.282 |
10 |
13.300 |
0.153 |
9.600 |
1.851 |
6.026 |
2.073 |
1.898 |
10.154 |
-1 |
| Experimental |
|
|
|
2nd quintile |
12 |
6.500 |
0.230 |
9.000 |
0.870 |
11.808 |
1.659 |
8.501 |
15.114 |
13 |
6.385 |
0.241 |
8.308 |
1.058 |
11.588 |
1.872 |
7.862 |
15.315 |
-1 |
| Experimental |
|
|
|
3rd quintile |
18 |
9.722 |
0.266 |
11.111 |
0.762 |
11.135 |
0.828 |
9.485 |
12.785 |
19 |
9.737 |
0.252 |
10.632 |
0.866 |
10.589 |
0.921 |
8.756 |
12.423 |
-1 |
| Experimental |
|
|
|
4th quintile |
7 |
13.571 |
0.202 |
11.714 |
1.107 |
8.413 |
2.036 |
4.355 |
12.471 |
7 |
13.571 |
0.202 |
11.714 |
1.107 |
7.871 |
2.326 |
3.241 |
12.501 |
0 |